The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Data Mining Practical Machine Learning Tools and Techniques

The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This paper presents an enhanced heuristic segmenter (EHS) and an improved neural-based segmentation technique for segmenting cursive words and validating prospective segmentation points respectively. The EHS employs two new features, ligature detection and a neural assistant, to locate prospective segmentation points. The improved neural-based segmentation technique can then be used to examine the prospective segmentation points by fusion of confidence values obtained from left and centre character recognition outputs in addition to the segmentation point validation (SPV) output. The improved neural-based segmentation technique uses a recently proposed feature extraction technique (modified direction feature) for representing the segmentation points and characters to enhance the overall segmentation process. The EHS and the neural-based segmentation technique have been implemented and tested on a benchmark database providing encouraging results.

/pdf/the-neural-based-segmentation-of-cursive-words-using-1k3kbnchoa.pdf

The neural-based segmentation of cursive words using enhanced heuristics

This paper proposes some directions for enhancing a neural network-based technique for automatically segmenting cursive handwriting. The technique fuses confidence values obtained from left and center character recognition outputs in addition to a Segmentation Point Validation output. Specifically, this paper describes the use of a recently proposed feature extraction technique (Modified Direction Feature) for representing segmentation points and characters to enhance the overall segmentation process. Promising results are presented for Segmentation Point Validation and cursive character recognition on a benchmark dataset. In addition, a new methodology for detecting segmentation paths is presented and evaluated for extracting characters from cursive handwriting.

Enhancing neural confidence-based segmentation for cursive handwriting recognition

This paper proposes a new sclera vessel recognition technique. The vesselpatterns of sclera are unique for each individual and this can be utilized to identify a person uniquely. In this research we have used a time adaptive active contour-based region growing technique for sclera segmentation. Prior to that, we have made some tonal and illumination correction to get a clearer sclera area without the distributing vessel structure. This is because the presence of complex vessel structures occasionally affects the region-growing process. The sclera vessels are not prominent in the images, so in order to make them clearly visible, a local image enhancement process using a Haar high pass filter is incorporated. To get the total orientation of the vessels, we have used Orientated Local Binary Pattern (OLBP). The OLBP images of each class are used for template matching for classification by calculating the minimum Hamming Distance. We have used the UBIRIS version 1 dataset for the experimentation of our research. The proposed approach has achieved high recognition accuracy employing the above-mentioned dataset.

/pdf/a-new-method-for-sclera-vessel-recognition-using-olbp-2egnhahk99.pdf

A new method for sclera vessel recognition using OLBP

Shark attacks have been a very sensitive issue for Australians and many other countries. Thus, providing safety and security around beaches is very fundamental in the current climate. Safety for both human beings and underwater creatures (sharks, whales, etc.) in general is essential while people continue to visit and use the beaches heavily for recreation and sports. Hence, an efficient, automated and real-time monitoring approach on beaches for detecting various objects (e.g. human activities, large fish, sharks, whales, surfers, etc.) is necessary to avoid unexpected casualties and accidents. The use of technologies such as drones and machine learning techniques are promising directions in such challenging circumstances. This paper investigates the potential of Region-based Convolutional Neural Networks (R-CNN) for detecting various marine objects, and Sharks in particular. Three network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG_M were considered for analysis and identifying their potential. A dataset consisting of 3957 video frames were used for experiments. VGG16 architecture with faster-R-CNN performed better than others, with an average precision of 0.904 for detecting Sharks.

Shark Detection from Aerial Imagery Using Region-Based CNN, a Study

The presence of multiple scripts in multi-lingual document images makes Optical Character Recognition (OCR) of such documents a challenging task. Due to the unavailability of a single OCR system which can handle multiple scripts, script identification becomes an essential step for choosing the appropriate OCR. Although, there are various techniques available for script identification from handwritten and printed documents having simple backgrounds, however script identification from video frames has been seldom explored. Video frames are coloured and suffer from low resolution, blur, complex background and noise to mention a few, which makes the script identification process a challenging task. This paper presents a study of various combinations of features and classifiers to explore whether the traditional script identification techniques can be applied to video frames. A texture based feature namely, Local Binary Pattern (LBP), Gradient based features namely, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) were used in the study. Combination of the features with SVMs and ANNs where used for classification. Three popular scripts, namely English, Bengali and Hindi were considered in the present study. Due to the inherent problems with the video, a super resolution technique was applied as a pre-processing step. Experiments show that the GLAC feature has performed better than the other features, and an accuracy of 94.25% was achieved when testing on 1271 words from three different scripts. The study also reveals that gradient features are more suitable for script identification than the texture features when using traditional script identification techniques on video frames.

/pdf/a-study-on-word-level-multi-script-identification-from-video-bkvewt0vzo.pdf

Michael Blumenstein

Papers

The neural-based segmentation of cursive words using enhanced heuristics

Enhancing neural confidence-based segmentation for cursive handwriting recognition

A new method for sclera vessel recognition using OLBP

Shark Detection from Aerial Imagery Using Region-Based CNN, a Study

A study on word-level multi-script identification from video frames